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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "# 柱状图\n",
    "# https://blog.csdn.net/hiudawn/article/details/80373996\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "N = 7\n",
    "\n",
    "x = np.arange(N)  # 即array([0,1,2...N])\n",
    "\n",
    "# 随机生成N个0到100之间的整数\n",
    "data = np.random.randint(low=0, high=100, size=N)\n",
    "\n",
    "# 随机生成几种颜色，reshape第二个参数-1指随着N变化，第一维度填满有剩就来填第二维\n",
    "# 生成的是随机的N组三通道(r,g,b)的颜色\n",
    "colors = np.random.rand(N * 3).reshape(N, -1)\n",
    "\n",
    "labels = ['Mon', 'Tue', 'Wed', 'Thu', 'Fri', 'Sat', 'Sun']\n",
    "\n",
    "plt.title(\"Weekday Data\")  # 设置窗格标题\n",
    "plt.bar(x, data, alpha=0.8, color=colors, tick_label=labels)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# 绘制出来两点之间相连都是直线\n",
    "plt.plot([1, 2, 3,4], [3, 6, 9,12], '-r')  # -是实线，r是红色\n",
    "plt.plot([1, 2, 3,4], [1, 5, 3,12], ':g')  # :是虚线，g是绿色\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "# 构造两组数据\n",
    "data = np.arange(100, 201)\n",
    "data2 = np.arange(200, 301)\n",
    "\n",
    "plt.subplot(1, 2, 1)  # 选中1行2列中的第1个绘图区\n",
    "plt.plot(data)\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.plot(data2)\n",
    "plt.show()  # 开始绘制窗口，可以理解为渲染"
   ]
  },
  {
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "# 构造两组数据\n",
    "data = np.arange(100, 201)\n",
    "data2 = np.arange(200, 301)\n",
    "\n",
    "fig = plt.figure()  # 创建图形窗口的意思\n",
    "# 设置布局\n",
    "ax = fig.add_subplot(121)  # 用一个变量接收1行2列中的第1个绘图区\n",
    "# ax = fig.add_subplot(1,2,1)  # 这个可以和上面的替换\n",
    "ax.plot(data)  # 在这个绘图区上作画\n",
    "\n",
    "ax = fig.add_subplot(122)\n",
    "ax.plot(data2)\n",
    "plt.show()"
   ],
   "cell_type": "code",
   "metadata": {},
   "execution_count": 3,
   "outputs": [
    {
     "output_type": "display_data",
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "# 散点图\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "N = 15\n",
    "\n",
    "# 新建3组点(x,y)，每组15个，x,y大小在区间[0,100]之间\n",
    "X1 = np.random.rand(N) * 100\n",
    "Y1 = np.random.rand(N) * 100\n",
    "\n",
    "X2 = np.random.rand(N) * 100\n",
    "Y2 = np.random.rand(N) * 100\n",
    "\n",
    "X3 = np.random.rand(N) * 100\n",
    "Y3 = np.random.rand(N) * 100\n",
    "\n",
    "plt.scatter(X1, Y1, c='r', s=100, alpha=0.5)  # c颜色，s大小，alpha透明度\n",
    "\n",
    "plt.scatter(X2, Y2, c='g', s=200, alpha=0.5)\n",
    "\n",
    "plt.scatter(X3, Y3, c='b', s=300, alpha=0.5)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ]
}